1. Combining Federated Learning and Control: A Survey
- Author
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Weber, Jakob, Gurtner, Markus, Lobe, Amadeus, Trachte, Adrian, and Kugi, Andreas
- Subjects
Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Systems and Control ,Statistics - Machine Learning - Abstract
This survey provides an overview of combining Federated Learning (FL) and control to enhance adaptability, scalability, generalization, and privacy in (nonlinear) control applications. Traditional control methods rely on controller design models, but real-world scenarios often require online model retuning or learning. FL offers a distributed approach to model training, enabling collaborative learning across distributed devices while preserving data privacy. By keeping data localized, FL mitigates concerns regarding privacy and security while reducing network bandwidth requirements for communication. This survey summarizes the state-of-the-art concepts and ideas of combining FL and control. The methodical benefits are further discussed, culminating in a detailed overview of expected applications, from dynamical system modeling over controller design, focusing on adaptive control, to knowledge transfer in multi-agent decision-making systems., Comment: This work has been submitted to the IEEE for possible publication
- Published
- 2024
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